@xrisk @pseudonym Volume is a key factor here. But even if the volume was the same, LLMs are doomed to stagnate as devs—whose code was scraped for training data—are displaced.

If you replace a junior with #LLM and make the senior review output, the reviewer is now scanning for rare but catastrophic errors scattered across a much larger output surface due to LLM "productivity."

That's a cognitively brutal task.

Humans are terrible at sustained vigilance for rare events in high-volume streams. Aviation, nuclear, radiology all have extensive literature on exactly this failure mode.

I propose any productivity gains will be consumed by false negative review failures.

If you replace a junior with #LLM and make the senior review output, the reviewer is now scanning for rare but catastrophic errors scattered across a much larger output surface due to LLM "productivity."

That's a cognitively brutal task.

Humans are terrible at sustained vigilance for rare events in high-volume streams. Aviation, nuclear, radiology all have extensive literature on exactly this failure mode.

I propose any productivity gains will be consumed by false negative review failures.

@malstrom @pseudonym that’s an interesting claim. I don’t know enough about LLM research to make a judgement. I do know that LLMs trained on synthetic (other LLM-generated) data tend to perform worse, but have we reached the limits of what LLMs are capable of? In my limited understanding, if an LLM can “learn” fundamental programming “concepts” (the same way they can “learn” concepts across human languages — I could be wrong in my understanding here), they should (might?) be able to transfer/apply those concepts to not-before-seen domains (maybe with a bit of “reasoning” prodded in).

@pseudonym That and LLM code often looks very nice on the surface so it takes a lot of vigilance and thinking to find the subtle errors. Code from juniors tends to have more immediate signs of errors or wrong mental models.

@pseudonym It's certainly like that.

FWIW though LLMs don't have any shame or feeling they need to manage their reputation.

If you tell the same LLM that produced the report that it is now the QA manager and it must review the report from the standpoints of checking for missing or inaccurate citations, dubious claims or non-concise text, it will rat itself out and can be told to fix what it found.

This is the same LLM entirely...

If you replace a junior with #LLM and make the senior review output, the reviewer is now scanning for rare but catastrophic errors scattered across a much larger output surface due to LLM "productivity."

That's a cognitively brutal task.

Humans are terrible at sustained vigilance for rare events in high-volume streams. Aviation, nuclear, radiology all have extensive literature on exactly this failure mode.

I propose any productivity gains will be consumed by false negative review failures.

If you replace a junior with #LLM and make the senior review output, the reviewer is now scanning for rare but catastrophic errors scattered across a much larger output surface due to LLM "productivity."

That's a cognitively brutal task.

Humans are terrible at sustained vigilance for rare events in high-volume streams. Aviation, nuclear, radiology all have extensive literature on exactly this failure mode.

I propose any productivity gains will be consumed by false negative review failures.

If you replace a junior with #LLM and make the senior review output, the reviewer is now scanning for rare but catastrophic errors scattered across a much larger output surface due to LLM "productivity."

That's a cognitively brutal task.

Humans are terrible at sustained vigilance for rare events in high-volume streams. Aviation, nuclear, radiology all have extensive literature on exactly this failure mode.

I propose any productivity gains will be consumed by false negative review failures.

@avuko @pseudonym The main reason that machine learning works so well with material and protein design, weather forecasting, and such, is that there is good data available to “train” the model. The internet is the source of LLM training. It is full of garbage and LLMs are filling it with more garbage. The rule is the same as in 1970: GIGO (garbage in, garbage out). Only the scale is different.

If you replace a junior with #LLM and make the senior review output, the reviewer is now scanning for rare but catastrophic errors scattered across a much larger output surface due to LLM "productivity."

That's a cognitively brutal task.

Humans are terrible at sustained vigilance for rare events in high-volume streams. Aviation, nuclear, radiology all have extensive literature on exactly this failure mode.

I propose any productivity gains will be consumed by false negative review failures.

@pseudonym Especially since the sort of mistake that LLMs make is the sort of mistake that's hardest for humans to spot. They produce bad code that looks like good code, because they were trained on a lot of good code and told "Write code that looks like this".

If you replace a junior with #LLM and make the senior review output, the reviewer is now scanning for rare but catastrophic errors scattered across a much larger output surface due to LLM "productivity."

That's a cognitively brutal task.

Humans are terrible at sustained vigilance for rare events in high-volume streams. Aviation, nuclear, radiology all have extensive literature on exactly this failure mode.

I propose any productivity gains will be consumed by false negative review failures.

@hopeless @pseudonym you are suggesting that you can just layer more shit onto the shit and after enough layers of shit it becomes not shit.

@pseudonym I have posed this conundrum before and the answer I received is that there is also an opportunity cost to not moving faster and the risk of a catastrophic bug may not outweigh the risk of being overtaken by competitors, especially since that was already happening before LLMs anyway.

Also, it *seems* models are improving at detecting these bugs, so they are being used to review changes, which, for the reasons you point out, they might be better at than people.

@pseudonym We are using AI inexactly the worst ways possible.

Caveat: I am a never AI-er, due to the ethical issues surrounding how training data is gathered, the severe ecological and economic impacts, and the fact that deepfakes are objectively making the world a shittier place.

But pretend for a second, none of those are a problem anymore. We are still using AI wrong. You don't have it produce a mountain of code and have a human review it. You still use humans to produce the code, and have AI help other humans to review it. AI isn't terribly good at writing code, but it has been shown to be effective at finding a few classes of bugs humans are typically very bad at finding.

But that won't allow you to fire people and replace them with monkeys on typewriters, so it'll never happen.

@nuintari what is AI?

Reason I ask is that for everything containing the least bit of software I can find a techbro willing to confabulate an 'ai' themed pitch deck. I'm not even kidding.

I surely hope to keep my dishwasher, if I promise not to call it 'ai' (but I'm sure someone else will)

If you replace a junior with #LLM and make the senior review output, the reviewer is now scanning for rare but catastrophic errors scattered across a much larger output surface due to LLM "productivity."

That's a cognitively brutal task.

Humans are terrible at sustained vigilance for rare events in high-volume streams. Aviation, nuclear, radiology all have extensive literature on exactly this failure mode.

I propose any productivity gains will be consumed by false negative review failures.

If you replace a junior with #LLM and make the senior review output, the reviewer is now scanning for rare but catastrophic errors scattered across a much larger output surface due to LLM "productivity."

That's a cognitively brutal task.

Humans are terrible at sustained vigilance for rare events in high-volume streams. Aviation, nuclear, radiology all have extensive literature on exactly this failure mode.

I propose any productivity gains will be consumed by false negative review failures.

If you replace a junior with #LLM and make the senior review output, the reviewer is now scanning for rare but catastrophic errors scattered across a much larger output surface due to LLM "productivity."

That's a cognitively brutal task.

Humans are terrible at sustained vigilance for rare events in high-volume streams. Aviation, nuclear, radiology all have extensive literature on exactly this failure mode.

I propose any productivity gains will be consumed by false negative review failures.